Type:
Educational Exhibit
Keywords:
Education and training, Computer Applications-Detection, diagnosis, Neural networks, Mammography, Computer applications, Breast, Artificial Intelligence
Authors:
E. F. Conant1, A. Y. Toledano2, S. Periaswamy3, S. Fotin3, J. Go3, J. Pike3, J. boatsman4, J. Hoffmeister3; 1Philadelphia/US, 2Kensington/US, 3Nashua, NH/US, 4San Antonio, TX/US
DOI:
10.26044/ecr2019/C-2151
References
1.
Skaane P,
Bandos AI,
Gullien R,
et al.
Comparison of digital mammography alone and digital mammography plus tomosynthesis in a population-based screening program.
Radiology 2013;267(1):47-56.
doi: 
10.1148/radiol.12121373
2.
Friedewald SM,
Rafferty EA,
Rose SL,
et al.
Breast cancer screening using tomosynthesis in combination with digital mammography.
JAMA 2014;311(24):2499-2507.
doi: 10.1001/jama.2014.6095
3.
Sharpe RE,
Venkataraman S,
Phillips J,
et al.
Increased cancer detection rate and variations in the recall rate resulting from implementation of 3D digital breast tomosynthesis into a population-based screening program.
Radiology 2016;278(3):698-706.
doi: 10.1148/radiol.2015142036
4.
Hooley RJ,
Durand MA,
Philpotts LE.
Advances in digital breast tomosynthesis.
AJR Am J Roentgenol 2017;208(2):256-266.
doi: 10.2214/AJR.16.17127
5.
Gilbert FJ,
Tucker L,
Gillan MGC,
et al.
Accuracy of digital breast tomosynthesis for depicting breast cancer subgroups in a UK retrospective reading study (TOMMY Trial).
Radiology 2015;277(3):697-706.
doi: 10.1148/radiol.2015142566
6.
Bernardi D,
Ciatto S,
Pellegrini M,
et al.
Application of breast tomosynthesis in screening: incremental effect on mammography acquisition and reading time.
Br J Radiol 2012;85(1020):e1174-1178.
doi: 10.1259/bjr/19385909
